The next high-priority phase of human genomics will involve the development of a full Haplotype Map of the human genome [12]. It will be used in large-scale screens of populations to associate specific haplotypes with specific complex genetic-influenced diseases. A prototype Haplotype Mapping strategy is presently being finalized by an NIH workinggroup. The biological key to that strategy is the surprising fact that genomic DNA can be partitioned into long blocks where genetic recombination has been rare, leading to strikingly fewer distinct haplotypes in the population than previously expected [12, 6, 21, 7]. In this paper

Abstract. It is widely anticipated that the study of variation in the human genome will provide a means of predicting risk of a variety of complex diseases. Single nucleotide polymorphisms (SNPs) are the most common form of genomic variation. Haplotypes have been suggested as one means for reducing the complexity of studying SNPs. In this paper we review some of the computational approaches that have been taking for determining haplotypes and suggest new approaches. 1

Abstract. It is widely anticipated that the study of variation in the human genome will provide a means of predicting riskof a variety of complex diseases. This paper presents a number of algorithmic and combinatorial problems that arise when studying a very common form of genomic variation, single nucleotide polymorphisms (SNPs). We review recent results and present challenging open problems. 1

Abstract Haplotypes are specially important in the study of complex diseases since they contain more information about gene alleles than genotype data. However, getting haplotype data via experiments methods is techniquely difficult and expensive. Thus, haplotype inference through computational methods is practical and attractive. There are several models for inferrings haplotype from population genotypes, of which we are interested in the pure parsimony model. This problem has been proved to be an NP-hard problem, so the goal of this paper is to design a heuristic method to obtain good solutions within acceptable time. A heuristic method based on genetic algorithm is presented for haplotype inference under pure parsimony criterion. The algorithm was tested on a variety of biological data and simulated data. In comparison with the exact algorithm HAPAR (based on a branch and bound algorithm), experiment results show that the method can obtain optimal solutions in almost all cases and runs much faster than HAPAR when the number of genotypes or SNP sites is large. It is suited for haplotype inference in relative large haplotype blocks because the algorithm is fast and its running time is not exponentially increased with input size.

Candidate gene pharmacogenetic studies offer a strategy for the rapid assessment of putative predictive markers. As a first step toward studying the pharmacogenetics of cancer chemotherapy, 51 candidate genes from the pathways of antineoplastic agents were resequenced to identify common genetic polymorphisms that might alter therapeutic response or toxicity. Forty DNA samples were screened from each of three population groups: African-Americans, Asian-Americans and European-Americans. Nearly 378 kb of genomic sequence was obtained from each sample. Nine hundred and four variants were identified, including 139 coding single nucleotide polymorphisms (cSNPs). Three hundred and fifty-six (40%) polymorphisms were common to all three populations and 366 (41%) were population specific. Three hundred and forty-six (38%) variants were novel polymorphisms that were not present in the three public databases that were examined. One hundred and eleven (35%) of the 319 non-synonymous cSNPs that were identified by either resequencing or database mining were predicted by PolyPhen to be either possibly or probably damaging. For the non-synonymous cSNPs identified by resequencing, both the number of cSNPs found and the maximum estimated allele frequency decreased with increasing predicted severity. These results provide experimental validation and estimated allele frequencies for polymorphisms in three common ethnic groups and facilitate applied pharmacogenetic studies of anticancer drugs.